Conditional Probability Research Articles

Mediating role of systemic inflammation in the association between volatile organic compounds exposure and periodontitis: NHANES 2011–2014

BackgroundVolatile organic compounds (VOCs) are ubiquitous environmental pollutants which have been suggested to have adverse effects on human health. While the influence of environmental pollutant exposures on periodontitis has attracted elevating attention in recent years, the epidemiological evidence on the association between VOCs exposure and periodontitis was scarce. This study aimed to investigate the potential mediating role of systemic inflammation factors in the complex association between VOCs exposure and periodontitis.MethodsUtilizing data from the National Health and Nutrition Examination Survey (NHANES) 2011–2014, we examined the impacts of VOCs exposure on periodontitis. Concentrations of urinary metabolites of VOCs (mVOCs) were measured using electrospray tandem mass spectrometry to evaluate internal VOCs exposure. Multivariable logistic regression, restricted cubic spline regression (RCS), Bayesian kernel machine regression (BKMR) and Quantile g-computation (QGC) models were performed to investigate the impacts of VOCs exposure on periodontitis. Mediation models were applied to assess the mediated effects of systemic inflammation on the association between mixed VOCs exposure and periodontitis. Besides, we analyzed the association between mixed VOCs exposure and periodontitis in stratified age, gender, and smoking status subgroups.Results1,551 participants were ultimately included for further analyses, of whom 45.20% suffering from periodontitis. Multivariable logistic regression and RCS identified positive associations between single urinary mVOCs and periodontitis (P < 0.05). Notably, BKMR and QGC models suggested that mixed VOCs exposure was significantly associated with periodontitis, with 2-Aminothiazoline-4-carboxylic acid (ATCA) contributing the most (conditional posterior inclusion probability = 0.997). Moreover, systemic inflammation markers (leukocyte and lymphocyte counts) were found to partly mediate the association between VOCs exposure and periodontitis (P < 0.05). No interaction effect was identified between mixed VOCs exposure and periodontitis in age, gender and smoking status subgroups (P > 0.05).ConclusionThis study demonstrated a positive association between VOCs exposure and periodontitis, which was potentially mediated by systemic inflammation factors. Further longitudinal researches are demanded to clarify the underlying mechanisms.

Probability Updating in the Classical and the Quantum Model

Imagine a Bayesian decision agent who is keen to invest only in technologies or businesses that are conductive to achieving a sustainable economic future. Being initially keen to invest in a certain technology, subsequently two pieces of new information are received that both individually reduce the agent’s inclination to make that investment. In such a situation, it would be natural to assume that the simultaneous consideration of both pieces of information should further reduce the agent’s initial enthusiasm; after all the Bayesian method has been called “ nothing but common sense reduced to calculation.” Somewhat surprisingly, making general statements like this about the double conditional probability requires substantial additional assumptions in classical Bayesian probability theory. We investigate four schemes of assumptions that allow ‘reasonable’ conclusions about the double conditional probability. We compare this with two schemes for the quantum probability model where the double conditional results from sequential updating through projections.

Estimating mean groundwater levels in peatlands using a Bayesian belief network approach with remote sensing data

Large-scale management, protection, and restoration of wetlands require knowledge of their hydrology, i.e., the status and dynamics of the groundwater table, which determine the evolution of the wetland ecosystem, its conservation value, and possible economic use. Unfortunately, in many cases, hydrological monitoring data are unavailable, resulting in the search for a proxy for the average annual depth of the groundwater level (GWL). This study presents an approach to estimating the mean GWL in peatlands using a Bayesian belief network (BBN) model, leveraging long-term hydrological and remote sensing data in the Biebrza National Park in Poland. The remote sensing data employed includes the synthetic aperture radar (SAR) backscatter coefficient, peat subsidence, rate and distance to watercourses. The BBN model achieved a predictive accuracy of 83.3% and 73.1%, depending on the validation approach used. Among the remote sensing variables considered, the SAR backscatter coefficient was the most sensitive in predicting the GWL in the peatlands. However, the model presents multiple uncertainties resulting from limitations of the available remote sensing data, low variability of class combinations in the conditional probability table, and lack of upscaling to other regions performed. Despite these uncertainties, the developed BBN model remains a valuable next step in reaching the goal of efficient peatland monitoring and management.

Variational Bayesian Monte Carlo simulation for rare event assessment

Estimating rare events in structural engineering demands intensive computational resources, and the complexity of the performance function poses challenges to accurate probability estimation. To efficiently conduct the structural reliability analysis on rare events, we propose a novel variational Bayesian Monte Carlo (VBMC)-based subset simulation (SS) method. The proposed approach can be divided into two aspects: the Monte Carlo simulation (MCS) is first applied to estimate the first-layer unconditional failure probability, then VBMC is employed to estimate subsequent conditional failure probabilities. Finally, the original small failure probability is obtained by the product of the first-layer unconditional failure probability and a series of conditional failure probabilities. The proposed approach inherits the merits of SS as well as VBMC, which converts the tricky rare event estimation into a series of high-frequency event estimations and leverages VBMC instead of the canonical Markov chain to produce the independent and informative next-generation failure-conditional samples. Four case studies, including high-dimensional and discrete state space scenarios, are performed to illustrate the feasibility and generality of the proposed method.

Simulation-based inference for parameter estimation of complex watershed simulators

Abstract. High-resolution, spatially distributed process-based (PB) simulators are widely employed in the study of complex catchment processes and their responses to a changing climate. However, calibrating these PB simulators using observed data remains a significant challenge due to several persistent issues, including the following: (1) intractability stemming from the computational demands and complex responses of simulators, which renders infeasible calculation of the conditional probability of parameters and data, and (2) uncertainty stemming from the choice of simplified representations of complex natural hydrologic processes. Here, we demonstrate how simulation-based inference (SBI) can help address both of these challenges with respect to parameter estimation. SBI uses a learned mapping between the parameter space and observed data to estimate parameters for the generation of calibrated simulations. To demonstrate the potential of SBI in hydrologic modeling, we conduct a set of synthetic experiments to infer two common physical parameters – Manning's coefficient and hydraulic conductivity – using a representation of a snowmelt-dominated catchment in Colorado, USA. We introduce novel deep-learning (DL) components to the SBI approach, including an “emulator” as a surrogate for the PB simulator to rapidly explore parameter responses. We also employ a density-based neural network to represent the joint probability of parameters and data without strong assumptions about its functional form. While addressing intractability, we also show that, if the simulator does not represent the system under study well enough, SBI can yield unreliable parameter estimates. Approaches to adopting the SBI framework for cases in which multiple simulator(s) may be adequate are introduced using a performance-weighting approach. The synthetic experiments presented here test the performance of SBI, using the relationship between the surrogate and PB simulators as a proxy for the real case.

Impacts of chronic diseases and multimorbidity on health-related quality of life among community-dwelling elderly individuals in economically developed China: evidence from cross-sectional survey across three urban centers

ObjectiveAs the ageing process in China further accelerates and the average life expectancy increases, chronic disease prevalence and multimorbidity rates are constantly rising, especially among elderly individuals. However, few previous studies have explored the impacts of chronic diseases and multimorbidity on health-related quality of life (HRQoL). This study aimed to investigate this association among community-dwelling elderly individuals in China.MethodsA cross-sectional study was conducted in communities in three cities (Suzhou, Qingdao, and Guangzhou). The basic characteristics, chronic diseases and HRQoL of participants were collected, and HRQoL was measured by the EuroQol 5-Dimensions 3-Level version (EQ-5D-3L). Logistic regression, Tobit regression and generalized linear models were used to assess the impacts of chronic diseases and multimorbidity on HRQoL.ResultsApproximately 83.2 percent of the 1,218 respondents had chronic conditions, with 30 percent having multimorbidity. After controlling for sociodemographic and health behaviour factors, patients with stroke were more likely to report problems in all five dimensions of the EQ-5D and had a lower EQ-5D utility index (UI) (b = -0.342) than patients with other chronic conditions. Patients with chronic pulmonary obstruction had a lower EuroQol Visual Analog Scale (EQ-VAS) (b = -11.169) than patients with other chronic conditions. Furthermore, patients with multimorbidity had worse HRQoL (P < 0.001).ConclusionsBoth chronic condition probability and multimorbidity rates were high among Chinese community-dwelling elderly individuals. Different disease types had varying degrees of impact on HRQoL, and patients with multimorbidity had worse HRQoL. This study proposes that the government enhance the quality of life of community-dwelling elderly individuals with multimorbidity by establishing long-term care insurance and expanding comprehensive community-based home health care services.

Air quality in different urban hotspots in a metropolitan city in India and the environmental implication.

This research study investigates hourly data on concentrations of five major air pollutants such as particulate matter (PM10, PM2.5) and gaseous pollutants (SO2, NO2, CO) measured during 2022 at four hotspot sites (industrial site, traffic site, commercial site, harbour, and one residential site) in Chennai, India. The analysis encompasses temporal variations spanning annual, seasonal, and diurnal variations in the pollutants. Notably, PM10 and CO emerge as the predominant pollutants, with the highest concentrations at industrial and traffic sites (PM10: 67.64 ± 40.77µg/m3, CO: 1.41 ± 0.84mg/m3; traffic site: PM10: 58.67 ± 20.05µg/m3, CO: 0.99 ± 0.57mg/m3). Seasonal dynamics reveal prominent winter spikes in particulate matter (PM10, PM2.5) and carbon monoxide (CO) concentrations, while nitrogen dioxide (NO2) and sulphur dioxide (SO2) levels peak during the summer season, particularly in the harbour area. The proximity to roadways exerts a discernible influence on diurnal patterns, with traffic sites showcasing broader rush hour peaks compared to sharper spikes observed at other sites. Furthermore, distinct bimodal patterns are evident for PM10 and PM2.5 concentrations in residential and harbour areas. A common lognormal distribution pattern is identified across the studied sites, suggesting consistent air quality trends despite contrasting locations. The conditional probability function (CPF) is used in conjunction with local meteorological conditions for identifying key pollution sources in each location. The implementation of polar plots emphasizes industries as principal local sources of pollution, at industrial sites significantly contributing to PM10, SO2, and NO2 concentrations under specific wind conditions. The main objective of the present study is to facilitate a good understanding of pollutant dynamics, pollution sources, and their intricate interplay with meteorological factors, thereby contributing to the formulation and implementation of effective air pollution control and mitigation strategies.

Natural frequency tree- versus conditional probability formula-based training for medical students’ estimation of screening test predictive values: a randomized controlled trial

BackgroundMedical students and professionals often struggle to understand medical test results, which can lead to poor medical decisions. Natural frequency tree-based training (NF-TT) has been suggested to help people correctly estimate the predictive value of medical tests. We aimed to compare the effectiveness of NF-TT with conventional conditional probability formula-based training (CP-FT) and investigate student variables that may influence NF-TT’s effectiveness.MethodsWe conducted a parallel group randomized controlled trial of NF-TT vs. CP-FT in two medical schools in South Korea (a 1:1 allocation ratio). Participants were randomly assigned to watch either NF-TT or CP-FT video at individual computer stations. NF-TT video showed how to translate relevant probabilistic information into natural frequencies using a tree structure to estimate the predictive values of screening tests. CP-FT video showed how to plug the same information into a mathematical formula to calculate predictive values. Both videos were 15 min long. The primary outcome was the accuracy in estimating the predictive value of screening tests assessed using multiple-choice questions at baseline, post-intervention (i.e., immediately after training), and one-month follow-up. The secondary outcome was the accuracy of conditional probabilistic reasoning in non-medical contexts, also assessed using multiple-choice questions, but only at follow-up as a measure of transfer of learning. 231 medical students completed their participation.ResultsOverall, NF-TT was not more effective than CP-FT in improving the predictive value estimation accuracy at post-intervention (NF-TT: 87.13%, CP-FT: 86.03%, p = .86) and follow-up (NF-TT: 72.39%, CP-FT: 68.10%, p = .40) and facilitating transfer of training (NF-TT: 75.54%, CP-FT: 71.43%, p = .41). However, for participants without relevant prior training, NF-TT was more effective than CP-FT in improving estimation accuracy at follow-up (NF-TT: 74.86%, CP-FT: 58.71%, p = .02) and facilitating transfer of learning (NF-TT: 82.86%, CP-FT: 66.13%, p = .04).ConclusionsIntroducing NF-TT early in the medical school curriculum, before students are exposed to a pervasive conditional probability formula-based approach, would offer the greatest benefit.Trial registrationKorea Disease Control and Prevention Agency Clinical Research Information Service KCT0004246 (the date of first trial registration: 27/08/2019). The full trial protocol can be accessed at https://cris.nih.go.kr/cris/search/detailSearch.do?seq=15616&search_page=L.

Ensemble probability distribution of annual runoff for the past 70 years in two main watersheds of China

ABSTRACT Water resources in China, especially in the major basins of the Songhua and Yangtze Rivers, are characterized by uneven distribution both temporally and spatially, leading to notable challenges in per capita water availability. In this study, we investigate the ensemble probability distribution of annual runoff over the past 70 years in two of China's major watersheds: the Songhua River and the Yangtze River. By dividing each basin into several regions from upstream to downstream, based on annual mean discharge as a proxy for annual runoff, we observed a significant correlation between the annual runoff of the control sections and that of the upstream and downstream regions in these large watersheds. Consequently, this study establishes the probability distribution of annual runoff for each region within the basins, anchored on the design annual runoff of the control sections, using the joint bivariate logarithmic normal distribution of two interrelated random variables. Furthermore, we developed a relationship between the conditional probability distribution and correlation diagram data, and a regression equation correlating regional annual runoff with the watershed control section's runoff. This investigation into the historical patterns of runoff over the past 70 years provides a comprehensive understanding of the dynamics in these critical watersheds.

PRO-SMOTEBoost: An adaptive SMOTEBoost probabilistic algorithm for rebalancing and improving imbalanced data classification

In the field of data mining and machine learning, dealing with imbalanced datasets is one of the most complex problems. The class imbalance issue significantly affects the classification of minority classes when using common classification algorithms. These algorithms often prioritize improving the performance of the majority class at the expense of the minority class, leading to misclassifying negative instances as positive ones. To address this problem, the Synthetic Minority Over-sampling Technique (SMOTE) has gained popularity to rebalance imbalanced data for classification. However, in this paper, we propose two algorithms to enhance the performance of imbalanced classification further. The first algorithm is PRO-SMOTE, an improvement over SMOTE. PRO-SMOTE relies on conditional probabilities to effectively rebalance imbalanced classes and improve the predictive performance metrics satisfactorily and reliably. By considering conditional probabilities, PRO-SMOTE can reduce the majority classes and optimally increase the minority class. Second, the PRO-SMOTEBoost algorithm, in turn, is based on the PRO-SMOTE to overcome classification anomalies and problems encountered by machine learning algorithms during classification, especially the weak ones. PRO-SMOTEBoost aims to maximize predictive precision to the greatest extent possible by combining the strengths of PRO-SMOTE with boosting techniques. Evaluating these algorithms using traditional machine learning algorithms such as Random Forests, C4.5, Naive Bayes, and Support Vector Machines has demonstrated excellent classification results. The performance metrics, encompassing F1-score, G-means, Precision, Accuracy, Recall, AUC-ROC, and Precision-Recall-curves, achieved by the proposed algorithm demonstrate a range that extends from over 90% to a flawless score of 100%. Compared to using these traditional algorithms individually, the utilization of PRO-SMOTEBoost has shown a significant improvement of 10% to 40% in performance metrics. Overall, the proposed algorithms, PRO-SMOTE and PRO-SMOTEBoost, offer effective solutions to address the challenges posed by imbalanced datasets. They provide improved predictive metrics and demonstrate their superiority when compared to traditional even modern machine learning algorithms.

The association between tobacco use and COVID-19 diagnoses in three Nordic countries: a pooled analysis.

Previous research has suggested an unexpected negative association between smoking and susceptibility to COVID-19. This study, drawing on population-based data from three Nordic countries-Sweden, Norway, and Finland-aims to investigate this association further, capitalizing on diversity introduced by different containment measures. The objective of this research was to examine the association between cigarette smoking and snus (smokeless tobacco) use and the risk of confirmed COVID-19 infection. A pooled analysis integrating original data from 547,685 participants across three countries. We used a multiple imputation approach based on conditional probabilities to impute the systematically missing covariates. The associations between tobacco use and COVID-19 infection were assessed, controlling for potential confounding factors. Current cigarette smokers had a lower risk of a confirmed COVID-19 case, whereas there was an increased risk among snus users. Our sensitivity analysis confirmed that the associations between tobacco use and COVID-19 infection risk are robust, remaining consistent regardless of whether covariate imputation was applied. Findings support a negative association between smoking and SARS-CoV-2 infection, but not the hypothesis that nicotine may be protective against the risk of contracting SARS-CoV-2 infection.

Trajectories of self-reported fatigue following initiation of multiple sclerosis disease-modifying therapy

BackgroundWe analysed the COMparison Between All immunoTherapies for Multiple Sclerosis (NCT03193866), a Swedish nationwide observational study in relapsing–remitting multiple sclerosis (RRMS), to identify trajectories of fatigue and their association with...

Bayesian network model of ethno-racial disparities in cardiometabolic-based chronic disease using NHANES 1999-2018.

Ethno-racial disparities in cardiometabolic diseases are driven by socioeconomic, behavioral, and environmental factors. Bayesian networks offer an approach to analyze the complex interaction of the multi-tiered modifiable factors and non-modifiable demographics that influence the incidence and progression of cardiometabolic disease. In this study, we learn the structure and parameters of a Bayesian network based on 20 years of data from the US National Health and Nutrition Examination Survey to explore the pathways mediating associations between ethno-racial group and cardiometabolic outcomes. The impact of different factors on cardiometabolic outcomes by ethno-racial group is analyzed using conditional probability queries. Multiple pathways mediate the indirect association from ethno-racial group to cardiometabolic outcomes: (1) ethno-racial group to education and to behavioral factors (diet); (2) education to behavioral factors (smoking, physical activity, and-via income-to alcohol); (3) and behavioral factors to adiposity-based chronic disease (ABCD) and then other cardiometabolic drivers. Improved diet and physical activity are associated with a larger decrease in probability of ABCD stage 4 among non-Hispanic White (NHW) individuals compared to non-Hispanic Black (NHB) and Hispanic (HI) individuals. Education, income, and behavioral factors mediate ethno-racial disparities in cardiometabolic outcomes, but traditional behavioral factors (diet and physical activity) are less influential among NHB or HI individuals compared to NHW individuals. This suggests the greater contribution of unmeasured individual- and/or neighborhood-level structural determinants of health that impact cardiometabolic drivers among NHB and HI individuals. Further study is needed to discover the nature of these unmeasured determinants to guide cardiometabolic care in diverse populations.

Co-occurring mental and substance use disorders in Australia 2020-2022: Prevalence, patterns, conditional probabilities and correlates in the general population.

Previous estimates from 2007 found that co-occurring mental and/or substance use disorders were a pervasive feature of Australia's mental health. Since that time there have been shifts and improvements in the conceptualisation and incorporation of co-occurring disorders in research and treatment settings. The current study provides up-to-date estimates on the prevalence of co-occurring mental and/or substance use disorders, highlights common patterns of co-occurrence, identifies significant correlates and examines any changes in the extent of co-occurring disorders since 2007. Data were from the two Australian National Surveys of Mental Health and Wellbeing conducted in 2020-2022 (N = 15,893) and 2007 (N = 8841). Descriptive statistics were estimated for the number of co-occurring conditions, correlations and pairwise conditional probabilities. Multinomial logistic and robust Poisson regressions were used to identify significant correlates and compare changes in co-occurring conditions across surveys. Approximately 46% of people with a mental or substance use disorder in the past 12 months experienced two or more diagnosable conditions. There was little evidence to suggest that the prevalence of co-occurring disorders has changed since 2007 (Prevalence Ratio (PR) = 1.08, 95% CI = 0.98-1.18). Subgroup analysis indicated that those aged 16-24 years were significantly more likely to experience any co-occurrence in 2020-2022 compared with those aged 16-24 years in 2007 (PR = 1.44, 95% CI = 1.17-1.77). Co-occurring mental and substance use disorders remain endemic in Australia. Indeed, they appear to be increasingly problematic in younger, more recent cohorts. The results suggest that continued effort is needed to develop and implement transdiagnostic interventions that target broad contextual and/or societal factors.

METHODOLOGY FOR NUMERICAL MODELING OF DETERMINING THE VALUES OF CONDITIONAL PROBABILITIES OF HITTING AIR TARGETS WHEN FIRING A MISSILE WITH A TELEVISION OPTICAL SIGHT OF A COMBAT VEHICLE

The article presents a methodology for numerical modeling of determining the values of conditional probabilities of hitting air targets when firing a missile with a combat vehicle's television optical sight. The conditional probabilities of hitting air targets are associated with the sloping range to the far limit of the kill zone of an anti-aircraft missile system. The effectiveness of the use of an anti-aircraft missile system with a television optical sight depends on the plane of air targets, the picture plane of firing, and the conditions of missile firing. The following factors were taken into account in the modeling: the color of air targets, the meteorological range of atmospheric visibility, the quality of combat performance of the combat vehicle personnel, and sunlight. The calculations take into account the technical characteristics of the stations and systems of the combat vehicle and missile. The values of the first kind of missile firing errors are presented, namely, the systematic components of the first kind of missile firing errors and their standard deviations of errors depending on the far limit of the kill zone of the anti-aircraft missile system. It is shown that the systematic component of the dynamic first-order missile firing errors increases due to the maneuvering of air targets, provided that the standard deviation of the first-order missile firing errors during the maneuvering of air targets remains unchanged. The values of the standard deviations of the first kind of missile firing errors depending on the far limit of the kill zone of the anti-aircraft missile system are given, taking into account the influence of interference on the combat vehicle's reconnaissance radar station and optical interference on the channel of the combat vehicle's television optical sight. The parameters of the first kind of missile firing errors, i.e., the values of the probabilities of the missile passing through the “tube” of the required radius of operation of the missile's radio fuse, were found. The values of the second kind of missile firing errors, namely the conditional probabilities of the missile's radio fuse, are considered. The mathematical expectation of the random value of the radius of the missile's radio fuse, which corresponds to air targets under the specified conditions, is established. The values of conditional probabilities of hitting various air targets under various conditions of firing at the far edge of the kill zone of the anti- aircraft missile system are analyzed and calculated. The obtained analytical expressions for the numerical modeling technique and the corresponding graphical material are presented.

PREDICTION OF REPEATED OSTEOPOROTIC FRACTURES OF THE THORACIC AND LUMBAR VERTEBRAE (EXPERIMENTAL STUDY)

Objective. To analyze the patterns of occurrence of postoperative recurrent vertebral compression fractures (RVCF) depending on the location of the primary fracture after puncture vertebroplasty. Methods. 520 case histories of patients with osteoporotic fractures of the thoracic and lumbar spine of various locations were analyzed. The total number of treated vertebral bodies was 1,458 (thoracic 596 — 40.88 %, lumbar 862 — 59.12 %). Most often, compression fractures of the spine occurred in the thoracolumbar junction (784 — 53.77 %). RVCF was diagnosed in 64 (12.31 %) people (95 % CI: 9.94–14.68 %) during follow-up examinations, after 1, 3, 6 or 12 months. since the operation. The study was carried out by 3 methods of forecasting: assessment of conditional probabilities, study of the significance of a primary fracture as a predictor of repeated fracture in a specific vertebra, analysis of associative rules and relationships Results. According to the 1st method, all conditional probabilities do not exceed 0.4 and the highest of them are the probabilities LI and LIV RVCF (0.39) with a primary ThVI fracture, and the probability of ThVIII RVCF (0.38) with a ThV primary fracture. Method II found only two regularities that allow an interpretation that corresponds to the purpose of our research. The third method revealed the associative rules and connections of the primary fracture of ThV with RVCF LI, ThXII; according to ThVI with RVCF LI, LII; in the case of ThVIII with RVCF LI, ThXII; ThX fracture with ThX, ThXI, ThXII, LII RVCF; under ThXI conditions with ThV, ThVII, ThVIII, ThIX, ThX, ThXII RVCF; for ThX with RVCF ThIX, ThXI, ThXII, LII; ThX fracture with ThX, ThXI, ThXII, LII RVCF. Conclusions. Prediction of repeated osteoporotic fractures is an important and topical problem of todayʼs vertebrology. Our study shows the most vivid patterns that are characteristic of the general sample of patients, namely: with a primary fracture of ThXI — a new deformation of ThVIII is possible; in the case of ThVII — new fractures of ThIX, ThXII, LI; for ThXII — injuries of ThXI; primary LI — new ThXII, LIV, LV.

Statistical evaluation of road traffic noise in complex sound environment by using fuzzy inference

In order to evaluate road traffic noise in a sound environment, it is necessary to predict the sound levels at evaluation points based on the observations at a reference point. In actual sound environment, road traffic noise shows various types of probability distribution of non-Gaussian distribution and nonstationary property. Furthermore, there potentially exist various nonlinear correlations in addition to the linear correlation between the sound levels at evaluation and reference points. Consequently, the relationship between both points cannot be represented by a simple linear model based on only the linear correlation and lower order statistics. In this study, a complex sound environment difficult to analyze by using usual structural method is considered. By introducing a nonlinear model based on conditional probability distribution with various correlations between the sound levels, and applying fuzzy inference, an evaluation method for road traffic noise is theoretically proposed in a suitable form for the complex sound environment with nonlinear, non-Gaussian and nonstationary properties. The effectiveness of the proposed method is experimentally confirmed by applying it to the observed road traffic noise data in a complex sound environment.

Serum uric acid trajectories and the risk of cardiovascular disease: a longitudinal association and pathway analysis.

Age-specifically longitudinal associations and potential pathways between serum uric acid (SUA) and cardiovascular disease (CVD) remained unclear. This study aimed to explore SUA trajectories in different age populations and to determine their associations and potential pathways with incident CVD. This prospective cohort included 41,367 participants from the Kailuan study, including 30,938 participants aged <55 years and 10,419 participants aged ≥55. The SUA trajectories during year 2006-2012 were identified by latent class growth models. Three SUA trajectories were identified in the overall, aged <55 and aged ≥55 years participants, as "low-stable" (51.9%, 54.4%, and 43.3%), "moderate-stable" (39.0%, 36.9%, and 45.6%), and "high-stable" (9.1%, 9.7%, and 11.1%), respectively. During a median follow-up of 6.75 years, incident CVD occurred in 2302 participants (5.56%). Overall, a high-stable trajectory was independently associated with a higher risk of CVD (hazard ratio [HR], 1.23; 95% [confidence interval], 1.06-1.42). Notably, the associations differed by age, a significant association was only observed in participants aged ≥55 years (HR, 1.29; 95% CI, 1.05-1.58), rather than those aged <55 years (HR, 1.08; 95% CI, 0.89-1.33). The addition of SUA trajectories to a baseline risk model for CVD improved the integrated discrimination improvement value (P < 0.05) and category-free net reclassification improvement value (P < 0.05). Bayesian network showed the conditional probability of high CVD risk associated with aging, elevated SUA trajectories, blood pressure, glucose, and inflammation was 15.5%. High-stable SUA trajectories were independently associated with an elevated risk of CVD, which is mainly induced by hypertension, diabetes, and inflammation, especially in participants aged ≥55 years.

A formal goodness-of-fit test for spatial binary Markov random field models.

Binary spatial observations arise in environmental and ecological studies, where Markov random field (MRF) models are often applied. Despite the prevalence and the long history of MRF models for spatial binary data, appropriate model diagnostics have remained an unresolved issue in practice. A complicating factor is that such models involve neighborhood specifications, which are difficult to assess for binary data. To address this, we propose a formal goodness-of-fit (GOF) test for diagnosing an MRF model for spatial binary values. The test statistic involves a type of conditional Moran's I based on the fitted conditional probabilities, which can detect departures in model form, including neighborhood structure. Numerical studies show that the GOF test can perform well in detecting deviations from a null model, with a focus on neighborhoods as a difficult issue. We illustrate the spatial test with an application to Besag's historical endive data as well as the breeding pattern of grasshopper sparrows across Iowa.

Determination of ecological flow based on probability distributions of annual and monthly flows

ABSTRACT Developing scientifically sound ecological flow is crucial for protecting river ecosystems. However, most of the existing hydrological methods for determining ecological flow make it difficult to consider both inter-annual and intra-annual changes in runoff. To compensate for this shortcoming, this paper proposes an innovative ecological flow determination method based on the probability distributions of annual and monthly flows. Marginal distributions of annual and monthly flows are first fitted, and the Copula function is used to create joint probability distributions of annual and monthly flows. Conditional probabilities for different monthly flow sizes under different annual flow conditions are then calculated based on Bayesian inference. The conditional probabilities are combined with the flow–duration curve-–based method to finalize the ecological flow process considering both intra- and inter-annual runoff changes. A case study was conducted in Jinsha River, China. The results show that the total annual ecological water demand of the Jinsha River is 1.50, 1.25, and 1.04 × 1011 m3 under annual flow scenarios of high, medium, and low flows, respectively, which provide a red line for the development of water resources and hydro-energy resources of the Jinsha River, as well as for the better protection of the natural plant and animal species.